code: def export_loop_13() -> None:
      # Given a tensor x of values [x1, ..., xN],
      # Return a sequence of tensors of
      #   [[x1], [x1, x2], ..., [x1, ..., xN]]
  
      seq_in = onnx.helper.make_tensor_sequence_value_info(
          "seq_in", onnx.TensorProto.FLOAT, None
      )
      seq_out = onnx.helper.make_tensor_sequence_value_info(
          "seq_out", onnx.TensorProto.FLOAT, None
      )
      cond_in = onnx.helper.make_tensor_value_info(
          "cond_in", onnx.TensorProto.BOOL, []
      )
      cond_out = onnx.helper.make_tensor_value_info(
          "cond_out", onnx.TensorProto.BOOL, []
      )
      iter_count = onnx.helper.make_tensor_value_info(
          "iter_count", onnx.TensorProto.INT64, []
      )
  
      x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
  
      x_const_node = onnx.helper.make_node(
          "Constant",
          inputs=[],
          outputs=["x"],
          value=onnx.helper.make_tensor(
              name="const_tensor_x",
              data_type=onnx.TensorProto.FLOAT,
              dims=x.shape,
              vals=x.flatten().astype(float),
          ),
      )
  
      one_const_node = onnx.helper.make_node(
          "Constant",
          inputs=[],
          outputs=["one"],
          value=onnx.helper.make_tensor(
              name="const_tensor_one",
              data_type=onnx.TensorProto.INT64,
              dims=(),
              vals=[1],
          ),
      )
  
      zero_const_node = onnx.helper.make_node(
          "Constant",
          inputs=[],
          outputs=["slice_start"],
          value=onnx.helper.make_tensor(
              name="const_tensor_zero",
              data_type=onnx.TensorProto.INT64,
              dims=(1,),
              vals=[0],
          ),
      )
  
      axes_node = onnx.helper.make_node(
          "Constant",
          inputs=[],
          outputs=["axes"],
          value=onnx.helper.make_tensor(
              name="const_tensor_axes",
              data_type=onnx.TensorProto.INT64,
              dims=(),
              vals=[0],
          ),
      )
  
      add_node = onnx.helper.make_node(
          "Add", inputs=["iter_count", "one"], outputs=["end"]
      )
  
      end_unsqueeze_node = onnx.helper.make_node(
          "Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
      )
  
      slice_node = onnx.helper.make_node(
          "Slice",
          inputs=["x", "slice_start", "slice_end"],
          outputs=["slice_out"],
      )
  
      insert_node = onnx.helper.make_node(
          "SequenceInsert",
          inputs=["seq_in", "slice_out"],
          outputs=["seq_out"],
      )
  
      identity_node = onnx.helper.make_node(
          "Identity", inputs=["cond_in"], outputs=["cond_out"]
      )
  
      loop_body = onnx.helper.make_graph(
          [
              identity_node,
              x_const_node,
              one_const_node,
              zero_const_node,
              add_node,
              axes_node,
              end_unsqueeze_node,
              slice_node,
              insert_node,
          ],
          "loop_body",
          [iter_count, cond_in, seq_in],
          [cond_out, seq_out],
      )
  
      node = onnx.helper.make_node(
          "Loop",
          inputs=["trip_count", "cond", "seq_empty"],
          outputs=["seq_res"],
          body=loop_body,
      )
  
      trip_count = np.array(5).astype(np.int64)
      seq_empty: List[Any] = []
      seq_res = [x[: int(i)] for i in x]
      cond = np.array(1).astype(bool)
      expect(
          node,
          inputs=[trip_count, cond, seq_empty],
          outputs=[seq_res],
          name="test_loop13_seq",
          opset_imports=[onnx.helper.make_opsetid("", 13)],
          input_type_protos=[
              onnx.helper.make_tensor_type_proto(
                  onnx.TensorProto.INT64, trip_count.shape
              ),
              onnx.helper.make_tensor_type_proto(
                  onnx.TensorProto.BOOL, cond.shape
              ),
              onnx.helper.make_sequence_type_proto(
                  onnx.helper.make_tensor_type_proto(
                      onnx.TensorProto.FLOAT, []
                  )
              ),
          ],
      )
  
file_path: onnx/onnx/backend/test/case/node/loop.py
imports: from typing import Any, List
  import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
label_desc: Use image node_id 2 for calling the Loop obj's underlying member method code with example usage: obj.export_loop_13() without return types